Production of Soluble and Functional Anti-TNF-α Fab' Fragment in Cytoplasm of E. coli: Investigating the Effect of Process Conditions on Cellular Biomass and Protein Yield Using Response Surface Methodology

Abstract

With the increasing dominance of monoclonal antibodies (mAbs) in the biopharmaceutical industry and smaller antibody fragments bringing notable advantages over full-length antibodies, it is of considerable significance to choose the most suitable production system. Although mammalian expression system has been the preferred choice in recent years for mAbs production, E. coli could be the favorable host for non-glycosylated small antibody fragments due to the emergence of new engineered E. coli strains capable of forming disulfide-bonds in their cytoplasm.In this study, non-glycosylated anti-TNF-α Fab' moiety of Certolizumab pegol, produced by periplasmic expression in E. coli in previous studies, was produced in the cytoplasm of E. coli SHuffle strain. The results indicated that it is biologically functional by testing the antigen-binding activity via indirect ELISA and inhibition of TNF-α induced cytotoxicity using MTT test. Major factors affecting protein production and, optimized culture conditions were examined by analyzing growth characteristics and patterns of expression in 24 h of post-induction cultivation and, optimization of culture conditions by response surface methodology considering temperature, time of induction and concentration of inducer in small (tube) and shake-flask scale. Based on the results, temperature had the most significant influence on functional protein yield while exerting different impacts in small and shake-flask scales, which indicated that cultivation volume is also an important factor that should be taken into account in optimization process. Furthermore, richness of medium and slower cellular growth rate improved specific cellular yield of functional protein by having a positive effect on the solubility of Fab' antibody.

Keywords: Certolizumab; Fab’ antibody fragment; Optimization; Response surface methodology; SHuffle E. coli strain.